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Introducing CustomerLake: The Agentic CDP | Ali Ghodsi Databricks CEO at Data + AI Summit

Databricks18 June 2026Watch on YouTube

Part of series

Ep. 4 · Databricks & Omnigent

Databricks-oprichters presenteren hun open-source agent-architectuur en toekomstvisie op AI.

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Description

This week at Data + AI Summit, Databricks announced CustomerLake, a new Agentic Customer Data Platform (CDP) natively embedded in Databricks. CustomerLake brings core CDP capabilities, including Customer 360, identity resolution, audience building, campaign automation, activation, and personalization, directly into the lakehouse where customer data, AI models, and governance already reside. With CustomerLake, marketing and data teams work together on a shared, governed foundation to turn customer data into always-on, 1:1 customer experiences. Instead of relying on manual campaign work and disconnected systems, marketers can deploy agents that continuously analyze behavior, decide, and act, delivering intelligent engagement at enterprise scale without creating new silos, duplicating sensitive data, or adding martech complexity. Learn more: https://www.databricks.com/blog/introducing-customerlake-agentic-cdp

What you'll learn

  • CustomerLake integrates CDP capabilities directly into the Databricks lakehouse, eliminating data silos and complex martech stacks.
  • AI agents in CustomerLake automate customer engagement by continuously analyzing behavior, making decisions, and taking actions without manual intervention.
  • Identity resolution and audience building happen on a shared, governed foundation where customer data, AI models, and governance already exist.
  • Marketing and data teams collaborate on one platform to scale personalized 1:1 customer experiences at enterprise scale, without duplicating sensitive data.

Frequently asked questions

What is CustomerLake and how does it differ from traditional CDP platforms?
CustomerLake is an agentic CDP natively embedded in Databricks that brings core capabilities like Customer 360, identity resolution, audience building, and campaign automation directly into the lakehouse. Instead of disconnected systems and manual work, marketers deploy AI agents that continuously analyze customer behavior and automatically take action.
How do marketing and data teams collaborate on CustomerLake?
Both teams work together on a shared, governed foundation in the lakehouse where customer data, AI models, and governance already exist. This eliminates the need for separate systems and ensures both teams operate with the same data source for campaign automation and personalization.
What are the benefits of using AI agents for customer engagement?
AI agents continuously automate customer engagement by analyzing behavior, making decisions, and taking action without manual intervention. This enables intelligent engagement at enterprise scale while avoiding new data silos or adding martech complexity.
Why aren't sensitive customer data duplicated in CustomerLake?
CustomerLake operates natively within the Databricks lakehouse where governance and security controls already exist. By embedding everything on one platform, sensitive customer data doesn't need to be copied to external systems, improving security and compliance.

Topics

agentic aicdpcustomer dataai applicationsautomation